Visualization of electrophysiology data

ABSTRACT

A method for visualization of electrophysiology information can include storing electroanatomic data in memory, the electroanatomic data representing electrical activity on an anatomic region within a patient&#39;s body over a time period. An interval within the time period is selected in response to a user selection. A visual representation of physiological information for the user selected interval can be generated by applying at least one analysis method to the electroanatomic data. The visual representation can spatially represented on a graphical representation of the anatomic region within the patient&#39;s body.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.13/128,123(Now U.S. Pat. No. 8,478,393), filed Aug. 22, 2011, entitledVISUALIZATION OF ELECTROPHYSIOLOGY DATA, which is a U.S. national stageentry under 35 U.S.C. 371 of International Application No.PCT/US2009/063937, which claims the benefit of U.S. Provisional PatentApplication No. 61/112,961, which was filed on Nov. 10, 2008, andentitled ANALYSIS OF CARDIAC ELECTRICAL ACTIVITY AND ASSOCIATEDWORKFLOWS, the entire contents of which applications are incorporatedherein by reference. This application is also related to InternationalApplication No. PCT/US09/63737, entitled VISUALIZATION OF PHYSIOLOGICALDATA FOR VIRTUAL ELECTRODES and filed Nov. 9, 2009, which is alsoincorporated herein by reference.

TECHNICAL FIELD

The invention relates generally to visualization of electrical activityof a patient, and more particularly to spatial visualization ofelectrophysiology data for a patient.

BACKGROUND

Electrophysiology data is used in the diagnosis and treatment of cardiacarrhythmias. Electrophysiology data can be gathered and displayed invarious ways, including with the use of electrophysiology catheters(both contact and non-contact), patches or other devices containingelectrodes placed in contact with the surface of the heart, orreconstruction using electrocardiographic or other means.

Electrophysiology data is used in the diagnosis and treatment of cardiacarrhythmias. Electrophysiology data can be displayed in the form ofelectroanatomic maps, which spatially depict electrophysiology data on arepresentation of an organ or a body surface; however, electrophysiologydata is used by clinicians treating arrhythmias both in the form ofelectroanatomic maps and also in other ways, for example by analyzingelectrophysiology data generated at certain points.

During complex arrhythmias, cardiac electrical activity can be extremelycomplex and difficult to analyze and/or interpret using standard signalprocessing techniques. An example of a complex arrhythmia is atrialfibrillation (or “Afib”), which can involve fast and irregularelectrical activity resulting in complex electrograms.

SUMMARY

The invention relates generally to visualization of electrical activity,and more particularly to spatial visualization of electrophysiology datafor a patient.

One aspect of the invention provides a method for visualization ofelectrophysiology information. The method includes storingelectroanatomic data in memory, the electroanatomic data representingelectrical activity on a surface of an organ over a time period. Aninterval is selected within the time period in response to a userselection. Responsive to the user selection of the interval, a visualrepresentation of physiological information is generated for the userselected interval by applying at least one method to the electroanatomicdata. The visual representation can be spatially represented on agraphical representation of a predetermined region of the surface of theorgan.

The at least one method can, for example, include preprocessing thesignals to be substantially optimal for advanced analysis, detectinglocal activations in a given signal, delineating the cycle length,extracting frequencies of highest dominance and regularity, andidentifying anatomic regions sustaining culprit circuits and otherregions of abnormal cellular properties (e.g., for repolarization)relevant to the arrhythmia. This information can be visualized in asite-specific manner or presented as electroanatomic maps, which may bestatic or include animations. Statistical and visualization techniquesto compare various processing outputs can also be implemented.

The systems and methods are equally applicable to analyzing cardiacelectrical potentials measured directly via catheter mapping during anelectrophysiology study, using an electrode patch during open heartsurgery, or reconstructed using electrocardiographic imaging or othermeans.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 depicts a block diagram of a system for visualizing physiologicaldata in accordance with an aspect of the invention.

FIG. 2 depicts a block diagram of a system for visualizing physiologicaldata for one or more virtual electrodes in accordance with anotheraspect of the invention.

FIG. 3 depicts an example of a GUI that demonstrates some types ofpreprocessing that can be implemented in a visualization systemaccording to an aspect of the invention.

FIG. 4 depicts an example of a GUI that demonstrates additional types ofpreprocessing that can be implemented in a visualization systemaccording to an aspect of the invention.

FIG. 5 depicts graphs demonstrating methods relating to signal splicingthat can be implemented according to an aspect of the invention.

FIG. 6 depicts an example of a GUI that demonstrates preprocessing thatcan be implemented for selecting multiple intervals for splicing signalsin a visualization system according to an aspect of the invention.

FIG. 7 is a graph depicting amplitude as a function of time for acquiredsignals demonstrating filtering and baseline drift correction that canbe implemented according to an aspect of the invention.

FIG. 8 is another graph depicting amplitude as a function of time fordifferent acquired signals demonstrating filtering and baseline driftcorrection that can be implemented according to an aspect of theinvention.

FIG. 9 depicts an example of a graphical user interface demonstratingvisualization of activation time information according to an aspect ofthe invention.

FIG. 10 is an example of an electrogram demonstrating plural activationtimes and corresponding cycle lengths that can be determined accordingto an aspect of the invention.

FIG. 11 depicts an example of graphs demonstrating an approach that canbe utilized for determining activation times according to an aspect ofthe invention.

FIG. 12 depicts an example of a graph of a power spectrum.

FIG. 13 depicts an example of a graphical user interface demonstratingvisualization of frequency information that can be implemented accordingto an aspect of the invention.

FIG. 14 depicts an example of a graphical user interface demonstratingvisualization of region of interest analysis that can be implementedaccording to an aspect of the invention.

FIG. 15 depicts an example of a graphical user interface demonstratingvisualization of complex fractionated electrograms that can beimplemented according to an aspect of the invention.

FIG. 16 depicts an example of a graphical user interface demonstratingvisualization of propagation of electrical signals that can beimplemented according to an aspect of the invention.

FIG. 17 depicts an example computing environment that can be used inperforming methods and processing according to an aspect of theinvention.

DETAILED DESCRIPTION

The invention relates generally to visualization of electrical activityof a patient, and more particularly to spatial visualization ofelectrophysiology data for a patient.

In one embodiment, electroanatomic data can be stored in memory torepresent electroanatomic data for electrical activity on a surface ofan organ over a time period. Tools are provided to allow a user toselect an interval within the time period. Responsive to the userselection of the interval, a visual representation of physiologicalinformation is generated for the user selected interval by applying atleast one method to the electroanatomic data. Various methods can beused to provide respective spatial visualizations of desiredphysiological information. For instance, the visual representation canbe spatially represented (e.g., as an electroanatomic map) on agraphical representation of a predetermined region of the surface of theorgan. As a further example, one or more virtual electrodes can bepositioned on a point on the organ surface to provide a correspondingelectrogram or a plot of other physiological information for theelectrode(s). Thus, a user can utilize spatially relevant informationgleaned from the electrogram or a plot of other physiologicalinformation to guide selection of the interval. The user can furthervary the interval to dynamically change the resulting visualrepresentation.

As used herein, the term “virtual” in the context of electrodes or otherselected anatomical locations means that the selected location orstructure is not a physical electrode construction, but instead isparameterized by data (e.g., as mathematical model) at a point, acollection of points or a substantially continuous surface region thatis selected by a user. The resulting visual representation of thephysiological data for a given virtual electrode thus can representelectrophysiology data that has been acquired, that has been computed ora combination of acquired and computed data for a set of one or moregeometrical points associated with a surface region of the patient.

Those skilled in the art will appreciate that portions of the inventionmay be embodied as a method, data processing system, or computer programproduct. Accordingly, these portions of the invention may take the formof an entirely hardware embodiment, an entirely software embodiment, oran embodiment combining software and hardware. Furthermore, portions ofthe invention may be a computer program product on a computer-usablestorage medium having computer readable program code on the medium. Anysuitable computer-readable medium may be utilized including, but notlimited to, static and dynamic storage devices, hard disks, opticalstorage devices, and magnetic storage devices.

Certain embodiments of the invention are described herein with referenceto flowchart illustrations of methods, systems, and computer programproducts. It will be understood that blocks of the illustrations, andcombinations of blocks in the illustrations, can be implemented bycomputer-executable instructions. These computer-executable instructionsmay be provided to one or more processors of a general purpose computer,special purpose computer, or other programmable data processingapparatus (or a combination of devices and circuits) to produce amachine, such that the instructions, which execute via the processor,implement the functions specified in the block or blocks.

These computer-executable instructions may also be stored incomputer-readable memory that can direct a computer or otherprogrammable data processing apparatus to function in a particularmanner, such that the instructions stored in the computer-readablememory result in an article of manufacture including instructions whichimplement the function specified in the flowchart block or blocks. Thecomputer program instructions may also be loaded onto a computer orother programmable data processing apparatus (see, e.g., FIG. 17) tocause a series of operational steps to be performed on the computer orother programmable apparatus to produce a computer implemented processsuch that the instructions which execute on the computer or otherprogrammable apparatus provide steps for implementing the functionsspecified in the flowchart block or blocks.

FIG. 1 depicts an example of a system 10 for visualizing physiologicaldata of a patient. The system 10 can be implemented in a standalonecomputer, a workstation, an application specific machine, or in anetwork environment in which one or more of the modules or data canreside locally or remotely relative to where a user interacts with thesystem 10.

The system 10 includes patient data 12 for one or more patient, such ascan be stored in an associated memory device (e.g., locally orremotely). The patient data 12 can include electroanatomic data 14 thatrepresents electrical information for a plurality of points, each ofwhich is indexed or otherwise associated with an anatomical geometry ofthe patient. The patient data 12 can also include patient geometry data16, such as can be embodied as a patient geometry model. In oneembodiment, the patient geometry data can correspond to a surface ofmodel of a patient's entire organ, such as the heart, which can begraphically rendered as a two- or three-dimensional representation.

The patient electroanatomic data 14 can be raw data, such as has beencollected from an electrophysiology mapping catheter or other means thatcan be utilized to gather electrophysiology data for a selected regionof a patient (e.g., of an organ, such as the heart). Additionally oralternatively, the electroanatomic data 14 can correspond to processeddata, such as can be computed from raw data to provide electrophysiologyinformation for the selected region of the patient (e.g., a surface ofan organ, such as the heart).

By way of example, a contact or non-contact electrophysiology cathetercan be placed in a patient's heart and collect electrophysiology data ata plurality of spatial locations over time, such as during a number ofone or more cardiac intervals. Such data can be spatially andtemporarily aggregated in conjunction with image data for the patient'sheart to provide the electroanatomic data 14 for the patient's heart.Alternatively, other devices (e.g., catheters or patches) can be placedon or near a patient's heart, endocardially and/or epicardially, such asduring open chest and minimally invasive procedures, to recordelectrical activity data, which can be mapped to a representation of thepatient's heart to provide similar corresponding electroanatomic data14.

As another example, non-invasive electrophysiological mapping (e.g.,electrocardiographic imaging for the heart) can be performed on thepatient to generate the electroanatomic data 14. This technique cangenerate electrophysiological data by combining body surface electricalmeasurements with patient geometry information through an inverse methodprogrammed to reconstruct the electrical activity for a predeterminedsurface region of the patient's organ. Thus the results of the inversemethod can provide the corresponding electroanatomic data 14 that isregistered with (or indexed) relative to patient geometry data 16.

Those skilled in the art will understand and appreciate that the system10 is equally applicable to patient electroanatomic data 14 that can begathered and/or derived by any of these or other approaches, which maybe invasive or non-invasive. Additionally, it will be understood andappreciated that the electroanatomic data 14 can be provided in any formand converted into an appropriate form for processing in the system 10.

In addition to the patient electroanatomic data 14 related to thepatient's organ, the system 10 also employs the patient geometry data16, such as can represent a predetermined surface region of ananatomical structure of a patient. For example, the patient geometrydata 16 can correspond to a patient-specific representation of a surfaceof an organ or other structure to which the patient electroanatomicaldata has been registered. For instance, the patient geometry data 16 mayinclude a graphical representation of a region of the patient's organ,such as can be generated by appropriate imaging processing of image dataacquired for the patient. Such image processing can include extractionand segmentation of an organ from a digital image set. The segmentedimage data thus can be converted into a two-dimensional orthree-dimensional graphical representation of a surface region of thepatient's organ. Alternatively, the patient geometry data 16 cancorrespond to a mathematical model of the patient's organ that has beenconstructed based on image data for the patient's organ. Appropriateanatomical or other landmarks can be associated with the organrepresented by the anatomical data for the organ to facilitatesubsequent processing and visualization in the system 10.

As mentioned above, the electroanatomic data 14 can be registered into acommon coordinate system with the patient geometry data 16. Forinstance, the electroanatomic data 14 can be stored in a data structureof rows (corresponding to different anatomical points) and columns(corresponding to samples) in which the rows of data have the same indexas (or are registered to) respective points residing on patient geometrydata 16. This registration or indexed relationship between theelectrical data 14 and the patient geometry data 16 is indicated by adashed line at 18. In one embodiment the samples in each of the columnscan represent simultaneous information across the entire surface region(e.g., the heart) of the patient.

The patient geometry data 16 can be generated from image data that isacquired using nearly any imaging modality. Examples of imagingmodalities include ultrasound, computed tomography (CT), 3D Rotationalangiography (3DRA), magnetic resonance imaging (MRI), x-ray, positronemission tomography (PET), and the like. Such imaging can be performedseparately (e.g., before or after the measurements) utilized to generatethe electroanatomic data 14. Alternatively, imaging may be performedconcurrently with recording the electrical activity that is utilized togenerate the patient electroanatomic data 14 or the imaging.

It will be understood and appreciated by those skilled in the art thatthe system 10 is equally applicable to employ anatomical data that maybe acquired by any one of these or other imaging modalities.Alternatively or additionally, the patient geometry data 16 cancorrespond to a generic or custom representation of an organ, which maynot be the patient's own organ. In such a case, the electroanatomic data14 can be mapped (via registration 18) to the representation of theorgan according to identified anatomical landmarks. A manual,semi-automatic or automatic registration process can be employed inorder to register the anatomical model with the signal acquisitionsystem, if any.

It further will be understood and appreciated that depending upon theformat and type of input data appropriate formatting and conversion to acorresponding type of representation can be implemented by the system10. For instance, the patient data 12 can include electroanatomical datathat is provided to the system 10 in a known format or be converted to astandard format for processing by the system. Thus, the patient data 12can include an aggregate set of electroanatomical data for the patient.

The system 10 includes an analysis system 20 that is programmed withmethods 22, such as are programmed to identify, classify and visualizeorigins and mechanisms relating to patient electrical activity. Theanalysis system 20 provides output results data to an output device 24that generates a corresponding visual representation 26 based on theoutput results data. The methods 22 can also be utilized to providereliable and consistent targets for ablation and during all complexarrhythmias. Methods 22 can also be utilized to monitor change andorganization of atrial activity such as during preoperative,intra-operative monitoring, and post-operative follow up. The system canemploy methods that compute information for previously acquired data aswell as data that is being acquired for real-time (or near real-time)analysis. For instance, the methods are useful for monitoring cardiacactivity during ablation, analyzing ablation outcomes, and for postablation follow up.

The system 10 also can include an interface 28 that can be employed toselect which methods and algorithms are used as well as to controlparameters associated with performing the methods and the resultingrepresentation 26 that is displayed on the output device 24. Forinstance, the user interface 28 can enable a user to select one or morealgorithms that are used to perform computations for use in populatingan output representation generated in accordance with an aspect of theinvention. A plurality of predefined and user-programmable algorithmscan be employed to generate corresponding physiological data that can bevisually represented to the user. Additionally, each of the algorithmscan be programmed to compute corresponding physiological data for anassigned virtual electrode that has been positioned relative to arepresentation of the patient's organ (based on position data) such asdescribed herein. A virtual electrode corresponds to a point on thepatient's anatomy (epicardially or endocardially) of interest for whicha selected method or combination of methods will be applied to patientelectrical data for generating a corresponding output representation.Furthermore, the user can dynamically assign one or more algorithms,including digital signal processing algorithms per lead on the virtualelectrode, or a general computation including statistical analysis onthe aggregate of all or some of the virtual electrode's leads.

Each of the methods 22 can employ a corresponding user interfaceelement, such as a graphical feature for activating a drop down menu ordialog. For instance, in response to activating the user interfaceelement for a given algorithm, a user can access a correspondingprogramming dialog, which the user can employ to define properties andconstraints associated with the selected algorithm. The properties caninclude setting one or more time intervals in response to which theresulting output data and corresponding visual representation aregenerated. The user interface 28 can also provide a user-entry dialogthrough which the user can enter associated constraints associated withthe selected algorithm. The constraints entry dialog for a givenalgorithm can include a mechanism to select a subset of the electrodesas well as to define input or output limits associated with thealgorithm.

Each of the methods 22 can define a type of information to be providedin the resulting output representation 26. The methods 22 can range incomplexity from providing types of data that can be measured by anactual electrode (e.g., electrical potential) to providing more complexstatistical and comparative types of information. One type of data canbe considered a look-up or measured value, such as including electricalpotential activation time or frequency of the electrophysiologicalsignals for the selected electrode configuration. Other types of datacan provide information that can be computed based upon such measuredvariables, such as including a gradient of any variable or thestatistics of variables, such as including a mean, maximum or minimum.It is to be appreciated that any such variables or statistics thereofcan be computed spatially with respect to the organ and the electrodeconfiguration that is positioned relative to the organ as well astemporally over a time period in which the electrophysiology data hasbeen acquired. The temporal range (or interval) can be defined as partof the constraints or properties for performing a given selected methodor algorithm.

Additionally, there can be multiple output representations and anarrangement of these displays and the type of information that is beingdisplayed can be controlled by the user. For instance, the userinterface 28 can be utilized to access graphics control methods that areprogrammed to control, for example, whether the output is in a textbased form or a graphical form that is superimposed over the selectorgan and relative to a graphical representation of the selectedelectrode configuration and/or waveform representation.

In addition to affording the user an opportunity to select any number ofone or more algorithms and apply such algorithms to any number of one ormore virtual electrode configurations, the methods 22 can include acompare function that can access methods and functions programmed togenerate comparative data. The comparative data can be a spatialcomparison (e.g., between different virtual electrodes or differentanatomic positions), a temporal comparison (e.g., the same virtualelectrodes at different instances in time), or a spatial-temporalcomparison (e.g., different virtual electrodes at different timeinstances), which can vary depending on the type of data that is beingcompared. The compared data further can compare similar types ofinformation derived for different virtual electrode configurations.

As an example of temporal comparative data, one or more measured valuesor derived values for a same given anatomic location (e.g.,corresponding to the same virtual electrode configuration) can becompared for different cardiac intervals. For instance, a user canemploy methods to provide a comparison of earliest activation time for afirst interval relative to the earliest activation time for a secondinterval, which results can be displayed on an output representationeither superimposed on the same representation of the patient's organ oras a side-by-side comparison on two separate representations of thepatient's organ.

As yet a further example, where the original data set has been acquiredas unipolar data, the methods can be used to generate correspondingbipolar data such as by subtracting electrophysiology data that had beendetermined between two different virtual electrodes. The two differentvirtual electrodes can be chosen automatically, such as each pair beingchosen by applying a nearest neighbor algorithm. Alternatively, thevirtual electrode pairs can be user definable, such as by using apointer and selecting the electrodes or by identifying the electrodes byname in a text based data entry method.

In addition to applying a set of predefined algorithms such as describedherein, the user interface 28 can also provide means for a user toconstruct a new user-defined algorithm. For example, a new algorithm canbe constructed to define one or more characteristics, which can includemeasured types of variables and derived types of variables as well as toprovide comparative relationships between selected electrodes. The newalgorithm can be provided according to a defined scripting language thatis utilized by the system.

One or more algorithms can also be utilized to dynamically determine oridentify one or more anatomical locations that meet user-definedcriteria, such as according to a user-selected algorithm or functionrange. The user can employ a method programmed to automatically generatethe visual representation to include set of virtual electrodes (or otheridentifiers, such as a map) at anatomic positions of the patient's organthat satisfy user-defined criteria.

As a further example, the user interface 28 can provide means for a userto select a location on a graphical representation provided from thepatient geometry data 16. The selected location can be translated tolocation data, such as can correspond to a three-dimensional position ina coordinate system that is consistent with the patient geometry data.As an example, a user can employ a cursor via a pointing device (e.g., amouse) to identify and select a location on the two-dimensionalgraphical representation of the patient geometry. The 2-D screenlocation can be translated to a corresponding 3-D position on the modelaccording to the selected location.

As mentioned above, the analysis system 20 is programmed to generate avisual representation output data by applying one or more of theanalysis methods 22 to the electroanatomic data 14 and patient geometrydata 16. For example, the output device 24 can be a display thatprovides the visualization 26 in the form of graphs, text or numericalvalues, which can be provided in separate windows adjacent to thegraphical representation of the patient geometry data 16. Additionallyor alternatively, the analysis system 20 can provides the output data toan output device that is configured to provide a corresponding visualrepresentation (e.g., in the form of a graph or numerical value) that isrendered as an object graphically superimposed relative to the graphicalrepresentation of the patient model, indicated by arrow 30.

FIG. 2 depicts an example of another system 50 for visualizingphysiological data for a patient. In the example of FIG. 2, features ofthe system 50 for performing preprocessing as well as analysis andgeneration of visual representations are depicted. The preprocessingportions of the system 50 can be utilized to preprocess and convert thedata to an appropriate form for an appropriate time interval such as caninclude one or more beats. The system 50 includes a preprocessinggraphical user interface (GUI) 52 that is responsive to user inputs. Thepreprocessing GUI 52 can include a plurality of selection mechanismseach of which can activate corresponding preprocessing methods 54.

In the example of FIG. 2, the preprocessing GUI 52 includes an intervalselector 56 that can be utilized to select one or more time intervalsthat may be of interest to the user. For example of electrocardiographicanalysis, the intervals can correspond to beats or cardiac cycles anyuser defined time interval (e.g., a portion of one or more cycles) overwhich electrical data 58 has been acquired. As another example, theintervals can be non-temporally contiguous intervals, such as can beaggregated to provide a spliced signal having an increased frequencyresolution due to the lengthened interval of the spliced signal.

As described herein with respect to FIG. 1, the patient electrical datacan be acquired by various techniques, including invasive as well asnon-invasive approaches. Thus, the patient electrical data 58 cancorrespond to substantially raw data that has been acquired for thepatient, such as representing signals acquired for each of a pluralityof electrodes.

The preprocessing GUI 52 can also include an electrode selector 60 thatis utilized to select which electrode or electrodes are to be utilizedto populate an output result set for use in further processing andanalysis. The electrode selection can be automated, manual or acombination of manual and automated. The electrode selector 60 can beprovided via a GUI that allows a user to selectively enable or disableeach of the plurality of electrodes that have been utilized to acquirepatient electrical data at a corresponding anatomic location. As anexample, a plurality of electrodes can be distributed over a patient'storso for acquiring electrical information during a sampling period.Thus, the electrode selector can be employed to set which sensor orsensors will be utilized to acquire and define the subset of the patientelectrical data 58. Automated methods can also be utilized to detect andremove bad channels.

The preprocessing GUI 52 can also include a filter selector 62 that canbe utilized to select one or more preprocessing filters 64 for theselected set of patient electrical data 58. The preprocessing filters,for example, can include software methods programmed to filter theelectrical data 58, such as including a low pass filter, DC removalfilter, a de-trending filter, or a Wilson Central Terminal (WCT) filter.Those skilled in the art will understand and appreciate other types offilters 64 that can be selectively activated or deactivated via thefilter selector 62.

As the filters are turned on or off or otherwise adjusted(parametrically), the filter methods 64 can be applied to the electricaldata 58 and generate a corresponding filtered set of the patientelectrical data (as also may be reduced according to the selectedinterval(s) and the electrodes that has been selected). After thefiltering, electrode/channel selection and time interval selection havebeen implemented, the preprocessing GUI 52 (or other means oractivation) can be utilized to activate an inverse method 66, such asdescribed herein. The inverse method 66 utilizes geometry data 68 alongwith the modified patient electrode data (for the selected timeinterval, selected channels and filtered) to generate correspondingelectrical anatomic surface data 70. The electroanatomic data 70 isindexed or registered relative to predefined surface region of apatient, such as an epicardial surface or an endocardial surface of apatient's heart.

Examples of inverse methods suitable for use with body surfaceelectrodes are disclosed in U.S. Pat. No. 6,772,004, entitled System andMethod for Non-invasive Electrocardiographic Imaging and U.S. patentapplication Ser. No. 11/996,441 (Now U.S. Pat. No. 7,983,743), entitledSystem and Method for Non-invasive Electrocardiographic Imaging, both ofwhich are incorporated herein by reference. It will be appreciated thatother approaches can be utilized to generate the electroanatomic data,which further may vary according to the mechanism utilized to acquirethe patient electrical data 58.

The visualization system 50 also includes analysis methods 72 that areprogrammed to provide results data for generating a representation 74 ofphysiological data relating to the patient's organ. The representation74 can be generated based the electroanatomic data 70 for the patient.The representation 74 may include graphics, text information, or acombination of graphics and text. It will be appreciated that therepresentation 74 provided by the system 50 is not limited to quantitiesactually measured or otherwise provided in the electroanatomic data 70,but can also correspond to electrophysiology data for a one or morevirtual electrodes as may be selectively positioned by a user.

The analysis methods 72 can provide results data to an output generator76 based on parameters established by a user, or preconfigured either byphysician preferences possibly per procedure type. The output generator76 is programmed to provide the representation(s) 74 in one or moreforms, which can vary depending on the type of data being displayed.

As one example, the output generator 76 includes a virtual electrode(VE) generator 78 for providing the visual representation 74 responsiveto a user selecting one or more locations in a predetermined surfaceregion of a patient. Each selected location can correspond to a locationin a coordinate system that can be defined or represented by thegeometry data 68. For instance by selecting a location in a patientgeometry coordinate system, corresponding electrical information in theelectroanatomic data 70 for the nearest geometrical point (or acollection of nearest points) can be utilized by the analysis methods 72to provide the results. The virtual electrode generator 78 in turngenerates the corresponding representation of physiological data fromthe results of the analysis.

The representations 74 provided by the virtual electrode generator 78can be considered spatially localized in response to a user selection,such as for providing data at a selected point (in the case of eachsingle point virtual electrode) as well as along a plurality ofuser-selected locations corresponding to a multi-point ormulti-dimensional virtual electrode. The representation of physiologicaldata 74 generated for each virtual electrode can be in the form ofgraph, text/numerical information or a combination of graphs andtext/numerical information. Any number of one or more representationscan be generated for each virtual electrode.

The output generator 76 also includes a map generator 80 that isprogrammed for generating physiological data in the form of anelectroanatomic map that is superimposed over the predetermined surfaceregion of the patient's organ. For example, the map generator 80 canrender one or more maps over the entire surface representation of thepatient's organ, such as can be a two-dimensional or three-dimensionalrepresentation thereof. For instance, the map generator 80 can beprogrammable in response to a user-selection (e.g., via drop downcontext menu) to select which type of electroanatomic map will begenerated.

The output generator 76 can provide the representation 74 as includinginformation similar to that which might be generated based onelectroanatomic data 70 provided according to any of the mechanismsdescribed herein, including temporal and spatial characteristics thatcan be determined from acquired patient electrical data. As describedherein, however, the system 50 enables the user to define aconfiguration of one or more virtual electrodes of a defined type and alocation of such catheter(s) relative to the organ for which therepresentation 74 will be generated.

Additionally or alternatively, the configuration and placement of thevirtual electrodes can be automatically selected by the analysis methods72, such as to place an arrangement of one or more virtual electrodes atdesired anatomic locations (e.g., landmarks), which can be defined byuser-defined parameters. The user can also set parameters and propertiesto define what type of output or outputs the representation 74 willinclude. Thus, once the electrophysiology data has been acquired for apatient (using any technique) and stored in memory as theelectroanatomic data 70, a user can employ the system 50 to virtualizephysiological data of interest for the patient's organ. These resultscan be accomplished without requiring the user of the system 50 toactually acquire any new electrophysiology data from the patient. Thus,the system 50 can be a powerful addition to existing electrophysiologysystems as well as can be utilized as a standalone system.

The system 50 includes an analysis user interface GUI 82 that isprogrammed to provide a human-machine interface for controlling andactivating the analysis methods 72. A user can employ the GUI 82 via auser input device (e.g., a mouse, keyboard, touch screen or the like) toenter user inputs to set parameters and variables as well as to controldisplay techniques and algorithms utilized by the analysis methods 72.

The user interface 82 can include a configuration component 84 that isutilized to define a configuration and arrangement of one or moreelectrodes for which one or more of the resulting representations 74will be constructed. For example, the configuration component 84 canprovide the user with an electrode configuration data set that includesa plurality of predefined electrode types. The predefined electrodeconfigurations can correspond to any number of one or moreelectrophysiology catheters, which may correspond to commerciallyavailable products. For example, the predefined electrode configurationscan correspond to any number of one or more electrode configurationsthat have been previously defined or constructed by a user or otherwisestored in memory as a library of available virtual electrodeconfigurations. As described herein, the available electrodeconfigurations can range from a single electrode (corresponding to asingle point) or a linear arrangement of electrodes (such as disposedalong a catheter or probe), two-dimensional (e.g., a patch or surfaceconfiguration) or three-dimensional electrode configurations (e.g.,representing a volumetric arrangement of electrodes). These and othervirtual electrode structures can be defined via the electrodeconfiguration component 84. A user can also specify the number ofelectrodes and spatial distribution of such electrodes for a givenconfiguration. As an example, a single electrode may be defined as adefault setting for a virtual electrode, which can be modified to adifferent configuration via the configuration GUI 84.

The user interface 82 also includes a location selection component 86.The location selection component 86 can be utilized to identify one ormore locations at which the selected electrode configuration (e.g.,comprising one or more electrodes) is to be positioned relative to thepatient's organ. For example, the location selection component 86 canemploy a GUI element, such as a cursor, that a user can position with apointing device (e.g., a mouse, touch screen and the like) to select acorresponding anatomical location on a graphical depiction of thepatient's geometry. For example, the location can be on a selectedsurface region of an organ, in the organ or proximal to the organ. Asdescribed above, the representation 74 can be generated for the selectedlocation based on the location data, electroanatomic data 70 for a givenvirtual electrode configuration.

As an example, in response to the user input, the location selectioncomponent 86 can cause a graphical representation of the selectedelectrode configuration (e.g., a single virtual electrode or anarrangement virtual electrodes, such as in the form of a catheter, apatch or other type electrophysiology measuring device) to be positionedat the selected location. The location selection component 86 can alsobe utilized to adjust the orientation (e.g., rotate) and position of theselected electrode structure relative to a two-dimensional orthree-dimensional coordinate system for an anatomical model of thepatient's organ. That is, as described herein, the selected location ofa cursor on an image can be translated to a position (e.g., in a threedimensional coordinate system) relative to known patient geometry.Additionally or alternatively, the location selection component 86 canprovide a list of one or more predefined common anatomical locations.The common locations can be programmable and include user-definedlocations as well as those known in the art to be useful locations forvisualizing electrical activity for the organ.

As a further example, in situations where a user is to define thevirtual electrode configuration as a catheter having a single electrodeor having a plurality of electrodes, the location selection component 86can be utilized to identify a location at which the catheter is to bepositioned. In response to the user identifying the location, theidentified location can be populated with a graphical representation ofthe virtual electrode structure superimposed over the graphicalrepresentation of the interactive surface region of patient anatomy.Additionally or alternatively, the cursor itself can also take on theform of the selected virtual electrode construct, such as while it movesacross a window in which the organ model is being displayed.

In addition to selecting a desired location at which a virtual electrodeis to be positioned, the location selection GUI 86 can provide means fora user to draw a contour or a closed surface at a desired location onthe graphical representation of the patient's organ (e.g., on the leftventricle of the heart). The resulting contour or closed surface canidentify a corresponding path or boundary for a virtual electrodestructure. For a contour, the length of the contour can be automaticallypopulated with an arrangement of virtual electrodes. Similarly, aninterior of the patch boundary can be automatically populated with anarrangement of electrodes. The spatial distribution and number ofelectrodes can be specified by the user (via the electrode configurationcomponent 84). The arrangement and spatial distribution of electrodesfor a given configuration can be uniform (e.g., as a default setting) orit may be non-uniform, as programmed by a user.

The user interface 82 can also include an output control component 88that is utilized to set output parameters and properties for eachrepresentation 74 that is generated. The output control component 88 canbe utilized to select one or more measured or derived electrophysiologyparameters that can be provided as part of the representation 74 basedon the electrode configuration data and location data for each virtualelectrode configuration. The output control 88 can be programmed toprovide the employ the same algorithm for each virtual electrode or,alternatively, different algorithms or constraints can be defined foreach virtual electrode. The results set for the selected output controlcan include electrical potentials (e.g., unipolar or bipolarelectrograms, activation times, frequency information (e.g., powerspectrum), and statistics relating to these as well other derivedquantities.

Another application of the output control component 88 can be toselectively swap electrode configurations for comparative purposes. Forexample, the output control component 88, individually or in combinationwith the electrode configuration component 84, can be employed to add orremove as well as to reposition two or more selected catheters relativeto the representation of the patient's organ to modify the resultsprovided in the output representation 74.

The output control 88 further may be utilized to implement comparativefunctions between algorithms, between temporal sets of differentelectrophysiology data or to otherwise constrain the resulting outputdata that is to be visualized on the output device.

By way of example, the output representation 74 may include thestatistics of activation within a region of the patient's organ (e.g.,as defined by placement of a 2-D virtual electrode or a virtual patch),including a minimum, a maximum, an average, and a standard deviation ofactivation time, a minimum, maximum, average and a standard deviation ofthe electrical potential. Those skilled in the art will appreciate thatother statistical analyses or properties may be part of or derived fromthe electroanatomic data 70.

As a further example, the output control 88 can be utilized to controlor establish a filter that controls what information will be utilized bythe analysis methods 72 to generate a corresponding outputrepresentation 74. For instance, a user can employ the output control 88to set an interval for ascertaining activation time or other constraintsfor each virtual electrode. As another example, an interval can be setby a user that is utilized to determine a dominant frequency for eachvirtual electrode. A corresponding dominant frequency map can also begenerated. Thus based on such constraints, locations (corresponding toanatomical positions) that satisfy such time limits or other constraintscan be determined and provided to the output device for displaygraphically (or otherwise) on a graphical representation of the organ.It will be thus appreciated that any type of data that can be measuredor computed for an electrode arrangement positioned relative to apatient's organ can be computed and be provided in a virtual environmentbased on electroanatomic data 70.

Preprocessing of Electrophysiology Signal Data

As explained herein with respect to FIGS. 1 and 2 various types ofpreprocessing can be performed on input electrical data, such asincluding interval selection and filtering.

FIG. 3 depicts an example of a portion of a pre-processing GUI 100 thatcan be implemented for the system 50 of FIG. 2. The preprocessing GUI100 includes interval selection and filter selection GUI elements. Inthe example of FIG. 3, an interval selection is determined by placementof calipers 102 and 104 in the waveform window 106. While two waveformsare illustrated in the window 106 in this example, those skilled in theart will appreciate and understand that any number of such waveforms canbe depicted in the GUI 100 and that the number of waveforms can beselected by the user. The interval calipers 102 and 104 can be initiallypositioned at a given location in the waveform window 106, such as inresponse to activating an interval selection user interface element 105.A user can adjust the caliper positions via a cursor (or other pointingelement) 108. Thus, by adjusting position of one or both of therespective calipers 102 and 104 a desired interval or beat can beselected by the user for further processing as described herein. While asingle interval is illustrated in FIG. 3 by calipers 102 and 104, it isto be understood and appreciated that any number of one or more suchintervals can be selected for additional types of processing (See, e.g.,FIGS. 4 and 5). The set of patient electrical signals for the selectedinterval are shown in a window 114 for each of the active channels.Thus, in the window 114, the signals for each of the channels for theselected time period (defined by the interval) are shown according tofiltering and other correction factors that are applied to the set ofsignals.

Also depicted in FIG. 3 are filter selection GUI elements 110, such ascorresponding to the filter selector 62 of FIG. 2. In the example ofFIG. 3, various types of corrections and filtering can be selectivelyperformed dynamically on patient electrical data, including are the WCTreference filter, low pass filtering, DC removal filter, de-trendingfilter, a high pass filter and baseline drift correction. Thus selectinga given filter activates corresponding filtering methods on the patientelectrical data.

Additionally, bad channel correction may be implemented via GUI elements112, such as if data appears outside of expected operating parameters.The bad channel correction GUI elements 112 can be activated toimplement an automatic method that detects and selects waveformsdetermined to correspond to bad channels (or bad electrodes).Additionally, waveforms can be selected manually for removal from thecorresponding graphical waveform window 114 if they appear anomalousrelative to the other waveforms. Those skilled in the art willappreciate various approaches that can be implemented to remove theanomalies or bad channels can be removed from the sensor data.

FIG. 4 depicts an example of a portion of a pre-processing GUI 120 thatcan be utilized to select an interval that includes more than onecontiguous cycle. The basic form of the GUI 120 is similar to that shownand described with respect to FIG. 3. Accordingly, identical referencenumbers refer to corresponding features previously introduced herein.

In the example of FIG. 4, a corresponding interval is defined by therelative position of calipers 122 and 124. A corresponding sample timeperiod of signal segments is shown in the adjacent display window 126.As described herein, the set of signal segments in the window representpatient electrical data within a corresponding time period selected viathe calipers 122 and 124. In this example, a single interval encompassesa plurality of beats or cycles for each of plurality of channels. Thenumber of channels represented as signal segments in the window 126 canbe controlled by the user as well as other types of preprocessing, asdescribed herein. By selecting a greater time period (e.g., more thanone cycle or beat) for subsequent analysis additional types of temporalanalysis and corresponding spatial visualization of information can berealized.

Signal Splicing

During analysis of unipolar atrial signals especially in the case ofatrial fibrillation, systems and methods can use segments of atrialsignals between ventricular complexes. During frequency analysis ofatrial signals, if the ventricular rate is too high then the length ofatrial segment is too short thereby affecting the frequency resolution.Length of the signal is directly related to the frequency resolution,for a given sampling frequency. For example, for a sampling frequency of1000 Hz and signal length of 1000 samples, the frequency resolution is 1Hz. Smaller segments of non-temporally contiguous segments data can bespliced or aggregated to obtain an improved frequency resolution.Frequency components introduced due to splicing time domain signals thatare not in reality continuous in time are negligible due to the relativerandomness in comparison to the dominant periodicity of the atrialsignals.

FIG. 5 depicts examples of signal segments 140, 142 and 144 and aresulting spliced signal 146 that can be determined according to themethods mentioned above. The signal segments 140, 142 and 144demonstrate amplitude as a function of time, which time can be selectedas described herein via an interval selector. Also depicted in FIG. 5are examples of power spectra 150, 152, 154 and 156 (e.g., power oramplitude versus frequency) for each of the signal segments 140, 142 and144 and the spliced signal 146, respectively. Also shown in the powerspectra 150, 152, 154 and 156 is a corresponding dominant frequency foreach of the respective signal segments 140, 142, 144 and the splicedsignal 146. Thus, it is demonstrated that the spliced signal segment 146contains increased resolution of frequency information due to thelengthier sample in the spliced signal.

FIG. 6 depicts an example of a preprocessing GUI 160 that is utilized toselect multiple non-temporally contiguous intervals of electrophysiologywaveforms that can be spliced together. The basic form of the GUI 160 issimilar to that shown and described with respect to FIG. 3. Accordingly,identical reference numbers refer to corresponding features previouslyintroduced herein.

In the example of FIG. 6, a plurality of non-temporally contiguousintervals are selected via the relative position of calipers 162, 164and 166. A corresponding sample time period of signal segments is shownin the adjacent display window is shown in the adjacent display window168. As described herein, the signals in the window represent patientelectrical activity for spliced signal segments for each of the channelsof patient electrical data. As a result, the signal segments provide anincreased resolution for frequency analysis, such as described herein.

Baseline Removal

Another feature of preprocessing is baseline correction or baselineremoval, such as can be activated on selected patient electrical datavia filtering user interface elements (e.g., filter functions 110 inFIGS. 3, 4 and 6).

Signals are often contaminated with baseline drifts that cannot beremoved with a simple high pass filter. The baseline correction methodinvolves a technique of signal conditioning using multiresolutionanalysis (MRA) using wavelet transforms to remove extraneous baselinedrifts while preserving the relevant signal content. During MRA of theECG signal using wavelets, scaling and wavelet functions are obtained,which are associated with half band low-pass and high-pass filters,respectively. Baseline drift is substantially removed by eliminating theparticular scaling coefficients of the wavelet transform that representthe level of the baseline drift.

FIGS. 8 and 9 depict representative plots of voltage amplitude as afunction of time demonstrating application of preprocessing techniques.FIG. 8 depicts a plot 200 that includes an original signal 202, afiltered signal 204 and a signal 206 corresponding to computed baselinedrift associated with the original signal. Similarly, FIG. 9 depicts aplot 210 that includes an original signal 212, a filtered signal 214 anda signal 216 corresponding to computed baseline drift associated withthe original signal. By identifying the baseline drift 206 and 216 foreach of a plurality of signals, corresponding scaling components of thewavelet transform can be eliminated to substantially remove the baselinedrift from each such signal.

By way of further example, the length of the ECG signal determines themaximum number of levels of decomposition for MRA. And each level ofdecomposition has a particular band of frequencies associated with it.For a given sampling rate, for example 1000 samples/sec, the ninth levelof decomposition will consist of frequency components from [0, 1] Hz andfor the tenth level of decomposition it would consist of frequencyranging from [0, 0.5] Hz. The baseline drift (low frequency components)appear on the scaling coefficients. For example drifts in the signalrepresenting frequencies below the 1 Hz band can removed by eliminatingthe scaling coefficients corresponding to ninth level of decomposition.

Analysis Methods and Associated Visualizations

The following are descriptions of some of the methods that can beutilized by an analysis system (e.g., FIG. 1 or 2) to providecorresponding spatial visualization of physiological data according toan aspect of the invention.

Activation Time

The analysis methods shown and described herein (e.g., FIGS. 1 and 2)can be programmed to compute an activation time for one or more selectedlocation on the surface of the patient's organ in response to auser-selected interval. The activation time can also be computed foreach point on the patient's organ for which electroanatomic data has beacquired or computed for a corresponding time period. A user thus canselect an interval in response to a user input (e.g., via pointing userinterface element). The interval can be modified in response to furtheruser inputs that change the interval or the interval may remain fixed ifno changes are made to the interval.

The activation times can be presented as site-specific data at anyspatial location on the heart using a virtual electrode functionassociated with the GUI. The data can be presented as a 3D map oranimation, showing wavefront propagation corresponding to spatiallydistributed activation times (e.g., a star map) such as shown anddescribed with respect to FIG. 16. A variation of this propagation mapis the activation cine map. Here the user selects a fixed cycle lengthinterval on the electrogram. The electrogram is moved step by stepthrough this interval to generate a dynamically changing visualizationof the isochrones map for each step of the electrogram through theinterval.

FIG. 9 depicts an example of a GUI 250 demonstrating a visualization ofactivation time presented as an isochrone map superimposed over agraphical representation of a surface of a patient's heart 254. In theexample GUI 250 of FIG. 9, a plurality of virtual electrodes 252A, 252B,252C, 252D, 252E, 252F, 252G and 252H have been positioned atuser-selected locations on representation of the patient's heart 254. Itwill be appreciated that, as described herein, any number orconfiguration of virtual electrodes can be positioned on the surface254. Adjacent to the window in which the surface representation 254 isdepicted, are additional analysis and evaluation tools.

In the example of FIG. 9, an electrogram window 256 is populated with anelectrogram 258A, 258B, 258C, 258D, 258E, 258F, 258G and 258H for eachof the virtual electrodes 252A, 252B, 252C, 252D, 252E, 252F, 252G and252H, respectively. Thus, each electrogram displays the electricalactivity (voltage versus time) according to the electroanatomical datadetermined for each point at which the respective virtual electrodes arepositioned.

Additionally depicted in FIG. 9 is a window 259 that includes powerspectrums graphs 260A, 260B, 260C, 260D, 260E, 260F, 260G and 260H foreach of the virtual electrodes 252A, 252B, 252C, 252D, 252E, 252F, 252Gand 252H. Power spectrum demonstrates frequency versus amplitude, suchas can be computed from the electrograms 258A, 258B, 258C, 258D, 258E,258F, 258G and 258H associated with each of the respective virtualelectrodes 252A, 252B, 252C, 252D, 252E, 252F, 252G and 252H.

Additionally in the example of FIG. 10, a coordinate axis A1 is depictedadjacent to the surface model demonstrating the relative orientation ofthe patient's heart model 254. A user can further rotate thethree-dimensional surface model 254 (e.g., via the cursor or other imagecontrols) to a desired orientation for selecting and applying virtualelectrodes to one or more selected surface region.

The GUI 250 also includes a mechanism to define a time interval relativein the electrogram window for which activation times can be computed.For example, in response to a user selecting an interval selection userinterface element (e.g., a button) 262 a caliper user interface element264, which defines start and stop times, can be provided onto eachelectrogram in the electrogram window 256. In response to such userselection activating the interval selection function, an activation timecan be computed according to the selected interval. An indication of theactivation time can be provided for each of the electrograms accordingto the specified interval. Activation time for each of a plurality ofpoints on the entire surface of the heart can be computed for theinterval and a corresponding activation (or isochrone) map can begenerated to spatially represent the activation times over the surface254, as shown. The isochrone map 268 depicts activation times that havebeen computed as a function of an interval selected (via GUI element orbutton 262) in the electrogram window 258. A graphical scale (or colorkey) 272 can be provided adjacent to the isochrone map 268 to inform theuser of what each shade or color in the map represents. The computedactivation time for each electrogram is also represented at 265A, 265B,265C, 265D, 265E, 265F, 265G and 265H for each respective electrogram258A, 258B, 258C, 258D, 258E, 258F, 258G and 258H.

As demonstrated in FIG. 10, the caliper user interface element 264 caninclude a first caliper that defines a first time for the interval and asecond caliper that defines a second time for the interval, such thatthe difference between times defines the time interval. This timeinterval can be common for each of the electrograms, as demonstrated inthe figure. A user further may modify the time interval resulting incorresponding changes to the activation time being displayed in theactivation map that is superimposed on the patient's heart. Forinstance, a user can employ the cursor 267 to selectively adjust one orboth of the calipers 264 to adjust the interval in the displayedelectrograms. In response to the changes in a given caliper, theselected interval of the electrograms changes. As the calipers 264 areadjusted, a corresponding activation time can be re-computed for each ofthe electrograms 258A, 258B, 258C, 258D, 258E, 258F, 258G and 258H forthe respective virtual electrodes. The activation time for each of theplurality of points (e.g., thousands of points) is similarly computed inresponse to the changes in the interval. A corresponding method forcomputing the frequency spectrum can also be reapplied in response tochanges in the user-selected interval. The visual representation (e.g.,the electroanatomic map superimposed on the heart) is dynamicallymodified responsive to changes in the user selected interval.

By way of further example, the activation time can be computed byanalyzing the change in voltage over time (e.g., dV/dt) within theselected time interval. Alternatively or additionally, such as dependingupon the type of waveform, wavelet analysis can be performed toascertain the activation time for a given waveform. For instance, inatrial fibrillation, however, the cycle lengths are not identifiablefrom the chaotic fractionated waveform, resulting in the need foractivation time methods which are independent of cycle length and canidentify one or more activation times in a given electrogram within aninterval or in real-time.

An analysis system according to an aspect of the invention can includemethods programmed to employ wavelet analysis to identify all localactivations present in the given electrogram. That is, wavelet analysiscan determine any number of one or more local activations that may existin a respective waveform. Such analysis utilizes specially designedwavelets (referred to as ‘intrinsic wavelets’). The intrinsic waveletsare, representative of local activation. Then, one or more electrogramsfrom all or select locations are decomposed via wavelet transformationwith step-wise scaled and translated versions of the intrinsic wavelet.Resultant wavelet coefficients are analyzed for each time frame of theelectrogram. At smaller scales, wavelet coefficients reflect the detailsin the signal including the intrinsic deflection. At lower to middlescales, wavelet coefficients reflect bundles of myocardium in theneighborhood of said electrogram(s). At larger scales, the wavelettransform is partial to the global structure of the electrogram. Localintrinsic deflections are omnipresent in the signal and thereforereflected through all decomposition levels. The multiple activation timedetection methods exploit this property to identify local activity andassign the corresponding time frame(s) as a plausible activationtime(s). If no time instant satisfies this wavelet criteria, noactivation time is assigned to that site. This further can helpautomatically identify regions of no activity (e.g., like scar tissue).

As a further example, variation of wavelet coefficient curves withrespect to time for each scale is computed. Peaks of the curve aredetected and dominant peaks within a specified threshold of the maximumpeak (‘peak-threshold’) are selected for each scale. Time frames withina specified tolerance or width (‘window-width’) at which peaks areregistered across all scales are selected as activation times.Translation can be at the electrograms' original sampling frequency orbe up-sampled or down-sampled. The peak-threshold can be adjusted toinclude fewer peaks or more peaks. Similarly, the window-width isuser-programmable and thus can be made more stringent or more flexible.

Multiple methods can be programmed for detecting activation time. Agiven one of the programmed methods can be selected depending on whetherthe cycle length of a given electrogram can be readily ascertained.Those skilled in the art thus will appreciate that different methods forcomputing activation time can be selectively employed to each of theelectrograms. For example, dV/dt computation can be utilized fordetermining activation time for each electrogram having an identifiablecycle length, whereas wavelet analysis can be utilized for electrogramfor which the cycle length cannot be ascertained.

FIG. 10 depicts an example of plots showing an electrogram 300, awavelet coefficient curve 308 and a plot of chained wavelet coefficients310, such as can be computed based on the methods described herein. Thecomputed activation times for the electrogram segment 300 aredemonstrated by stars at 302, 304 and 306. Those skilled in the art willunderstand that a system or method can be programmed to selectivelyimplement one or more methods of calculating activation, such asdescribed herein.

Additionally, activation times for a signal segment can be employed tocompute cycle length as well as related statistical quantities for eachof a plurality of points on a surface region of the patient's heart,including over the entire epicardial surface. By way of example, FIG. 11depicts an example of a selected interval of an electrogram waveform312. One or more activation times 314A, 314B, 314C and 314D can becomputed for the electrogram 280 based on any one or more of the methodsdisclosed herein. Thus, in the example of FIG. 11, four activation timeshave been determined for the interval of the electrogram. A temporaldistance between each sequential pair of electrograms corresponds to acycle length between the pair of activation times, indicated at 316A,316B and 316C. The number of activation times, the cycle lengthinformation and quantities computed based on such information can bespatially represented (e.g., on an electroanatomical map), such as shownand described herein.

It will be appreciated that such cycle length analysis is applicable forcomplex fractionated electrograms (CFE, see, e.g., FIG. 16) as well asother types of electrograms that may be characterized by simplerorganized rhythms. Additionally, while the methods described herein havebeen described as being utilized for unipolar electrograms, thoseskilled in the art will understand and appreciate that the same methodscan also be utilized for bipolar electrograms.

As also described herein, signal pre-processing to extract relevantfrequency components may be applied in certain cases. These includebaseline correction techniques, for example, including: DC offsetremoval via subtraction of the temporal mean, zero-correction of thebeginning and end of callipered waveform(s), high pass filtering at <1Hz cutoff, and adaptive filtering techniques like moving average andKalman filtering, which could be considered as effective lower frequencyband limiters. Low pass filters at >30 Hz cutoff and specialized filterslike the Savitzky-Golay filter can be considered as effective higherfrequency band limiters.

A variation of the wavelet analysis is to detect the absolute maximumtransform coefficient across the entire electrogram and assign thecorresponding time frame as the activation time. Another variation is toperform a further detailed wavelet analysis on time frames with dominantcoefficients at lower scales.

Frequency Analysis

Methods described herein outline ways of extracting and analyzing thefrequency spectrum of complex cardiac electrical activity. Frequencymapping facilitates the identification and localization of sites of fastand frequent activity that are associated with ‘triggers’ that sustainfibrillatory conduction. A first step in frequency analysis is toperform band-limiting the electrogram signal to a band of relevantfrequencies. Lower frequencies to be removed include baseline variationsdue to DC offset, respiration and others.

Baseline correction techniques including DC offset removal viasubtraction of the temporal mean, zero-correction of the beginning andend of callipered waveform(s), high pass filtering at <1 Hz cutoff,adaptive filtering techniques like moving average and Kalman filteringcould be considered as effective lower frequency band limiters. Low passfilters at >30 Hz cutoff and specialized filters like the Savitzy-Golayfilter can be considered as effective higher frequency band limiters.Band pass filters that limit the signal within bands of 0.5-30 Hz alsocan be considered instead of the above mentioned two step filteringprocess.

Next, Fast Fourier Transform (FFT) is used to extract the frequencyspectrum of the electrogram(s). FFT can be computed using the highestpower of 2 closest to the length of input signal (N) and the first(N+1)/2 points are extracted. Then the magnitude of the FFT is scaled bythe length so that it is not a function of N and then squared. Sinceonly the first half of the spectrum is needed to be used (as the secondhalf is redundant), the energy of the entire spectrum is factored in bymultiplying by two. If the DC component and Nyquist component exist(i.e., N is even), they are unique and are therefore not multiplied by2. The FFT can be performed at the signal's original sampling frequency(e.g. at 1000 Hz) or be up-sampled or down-sampled. The length of thesignal can be extended by zero-padding in certain cases to improvefrequency resolution. Electrogram segments not continuous in time can bespliced and frequency analysis performed on the resultant combinedsignal (described in section ‘splicing’). Frequency analysis ofelectrograms can be performed real time and continuously by usingtechniques for removal of intermittent ventricular activity (‘QRSsubtraction’).

The frequency spectrum computed at each spatial location on the heartcan be recalled by reverse lookup and through the virtual electrodefunction of the GUI. From the frequency spectrum of each electrogram,the strongest power is designated as the Dominant Frequency (DF). Anexample frequency spectrum, demonstrating power versus frequency isshown in FIG. 12.

FIG. 13 depicts an example GUI 330 in which mapping controls have beenactivated to depict a dominant frequency map superimposed on thegraphical representation of the patient's heart, indicated at 332. Inthe example of FIG. 11, four virtual electrodes 334A, 334B, 334C and334D are positioned at desired locations on a patient's heart.Corresponding electrograms 336A, 336B, 336C and 336D and power spectrumplots 338A, 338B, 338C and 338D are depicted in respective windows 340and 342.

Additionally in FIG. 13, the dominant frequency map provides spatialinformation about the dominant frequency over the surface of the heart332 according to a corresponding to a scale 344, such as can beimplemented as a color code or gray scale code. Thus, reference to thescale 344 when viewing the dominant frequency map of the heart 332demonstrates to the user the dominant frequency for each region of theheart.

The dominant frequency can vary according to the interval for which thedominant frequency is computed. Thus in FIG. 13, caliper user interfaceelements 348 can be provided in the electrogram window, for example, toenable a user to select or vary a time interval for which the dominantfrequency is calculated, such as via a pointer or cursor. The caliperuser interface element 348 can be activated for selecting the intervalin response to activating a corresponding interval selector userinterface element 350, such as a button or other user interface feature.

Also depicted in FIG. 13 is a user interface element (e.g., a drop downcontext menu) 352 that defines what type of electroanatomic map issuperimposed on the heart 332. Thus, in the example of FIG. 13, dominantfrequency is selected, resulting in the map shown. It will beappreciated that other types of maps could be selected by a user (viathe user interface element 352) for display superimposed on the heart,such as shown and described herein.

In one embodiment of the invention, the dominant frequencies in eachelectrogram are displayed spatially on the 3D geometry of the heart 332,which may be called a real-time spectral map (RTSM). Each frequency fromlowest to highest will be visually identified by a unique colormap.RTSMs from various electrogram segments can be compared visually inseparate cardioframes or statistically using measures includingdifference maps and correlation coefficients. The spatial variability,gradient and dispersion of dominant frequencies in each RTSM can also becomputed and, for example, presented as numerical data, queried throughthe GUI's virtual electrode or presented as a 3D map.

The temporal organization of the dominant frequency in the spectrum, orregularity, can be estimated mathematically, such as by dividing thearea under the narrow band of the dominant frequency (+/− band where thepower falls <50% of the maximum power at the dominant frequency) andeach of its harmonics with the total area within a specified band (e.g.,about 5 Hz). Such regularity measures of dominant frequency sites can beidentified spatially via observing and querying RTSMs. Thus, theregularity for a given virtual electrode can be computed and provided asa regularity index. A consistency or repeatability index can be derivedby comparing RTSMs from contiguous segments.

The dominant frequencies can also be presented as a 3D animation overtime, showing consistency and/or distribution of DF sites over segmentsof analyzed data —Star Spectral map.

Region of Interest Analysis

Another function that can be implemented via an appropriated GUI elementis a comparison function, which can be employed to compare theelectrical activity spatially and/or temporarily. For example, a usercan compare electrical activity for two different spatial locations onthe patient's heart for the same heartbeat. Additionally oralternatively, a user can compare electrical activity of the samespatial location for two different heartbeats. As an example a user cancompare an arrhythmia beat with a paced beat or an arrhythmia beat witha normal beat, such as for the same spatial location of the patient'sheart (e.g., corresponding to a virtual electrode). The results of thecomparison can be displayed to a user, such as in the form of a mapsuperimposed on a 3-D representation of the patient's heart similar tothe representations shown and described herein. The comparison can be anumerical comparison as well as any statistical type of comparison.

Additionally, as described herein, the systems and methods can beutilized interpretatively, such as during an EP study. For example, auser can compare beats for a given anatomical region of interest beforeablation, during an ablation procedure as well as after ablation. Theresults of such comparisons can be presented as a function of thecomparison as a map or as a plurality of displays.

By way of example, FIG. 14 depicts an example GUI 360 in which region ofinterest analysis has been activated for evaluation of electrophysiologyof one or more regions on a surface of a patient's heart 362. In theexample of FIG. 14 two regions have been identified for evaluation,indicated by dashed lines 364 and 366. Those skilled in the art willappreciate various methods that can be utilized to select the regions.As one example, a user can position individual virtual electrodes oneach region of interest 364 and 366, such as placing electrodes 368 inregion 364 and virtual electrodes 370 in region 366. In the illustratedexample, the region 364 corresponds to the patient's left ventricle andthe region 366 corresponds to the patient's right ventricle.

GUI controls 372 can be provided in an adjacent window, such as tocontrol the color, size and method utilized to mark each region ofinterest with the virtual electrodes. The GUI controls 372 can also beutilized to selectively remove or edit placement virtual electrodes orportions of each region.

By way of example, a selection mode can be entered for a given region byselecting a begin user interface element (e.g., a button) 374. Afterplacing a desired number of virtual electrodes on the region, a user endthe placement mode for that region via another user interface element376. Another user interface element 378 can be utilized for editing thenumber or distribution of virtual electrodes for a given region.

Other approaches can be employed to mark a region for analysis. Forinstance, a user can employ a drawing tool or similar user interfacefeature to identify each one or more region 364 and 366 on the heart362. Each identified region can then be automatically populated with anarrangement of virtual electrodes 368 and 370. The number and spatialdistribution of electrodes can be programmed by the user. As yet anotheralternative approach, a list of predefined anatomical landmarks (basedon patient geometry data) can be provided to the user for selection.Each selected landmark can be automatically populated with a set of oneor more virtual electrodes for analysis.

Once a region 364, 366 has been configured as a virtual electrodestructure, electrical information can be displayed in an adjacentdisplay window 380. Information associated with the electrical activityof each region can be provided according to the configuration andplacement of virtual electrodes. The information can include statisticalinformation for each region, such as the average, maximum and minimumactivation time. A corresponding electroanatomical map can also besuperimposed on the surface of the heart 362 (e.g., an isochrone mapdepicted in the example of FIG. 14). Those skilled in the art willunderstand and appreciate various other types of information that can becomputed and presented to the user based on the arrangement of virtualelectrodes 368 and 370, which can include numerical values as well asgraphical information.

Morphological Analysis of Electrograms

The relevance of complex fractionated electrograms (CFE) as ablationtargets has been clinically observed. These electrograms are foundmostly in regions of diseased or infarcted tissue, slow conduction areasor at pivot points where the fibrillatory wavelets turn around atfunctional or anatomical lines of block. In this subpart of theinvention, methods and algorithms are described to identify CFEs andclassify them as relevant or transient. A measure of fractionation ofthe electrogram or Fractionation Index (FI) may be derived by countingthe number of transitions in the signal, and combining it with thenumber of local activations as detected by the multiple activation timedetection algorithms elsewhere described in this invention disclosure. Amore stringent definition of FI could include amplitude thresholds andcycle lengths obtained from frequency analysis also described elsewherein this invention disclosure. In one embodiment of the invention,degrees of fractionations can be displayed spatially as 3D complexfractionated electrogram maps (CFEM). Lowest to highest degrees offractionations can be visually identified by a unique colormap. CFEMsfrom various electrogram segments can be compared visually in separatecardioframes or statistically analyzed using measures includingdifference maps and correlation coefficients. The spatial variability ofeach CFEMs can also be quantified and used to sort out relevant CFEregions as treatment targets.

The shape and morphology of the electrogram can be analyzed and theclassified into known or typical morphologies like RS, rS, QRS, qRS, QS,RSR, etc. This classification can be color coded and displayed as a 3Dmap. Those familiar to EP mapping will recognize that in complexarrhythmias with complex electrogram, this kind of spatial morphologymapping will enable the physician to sort out benign vs. culprit sites,e.g., late activation regions can be excluded and early activationpossibilities can be further analyzed.

Cycle Length and Cycle Length Variability (CLV)

As shown and described with respect to FIG. 10, cycle length can beestimated or calculated from multiple activation time methods, such asshown and described herein. As mentioned, the time difference betweeneach pair of contiguous or sequential activations constitutes acorresponding cycle length. A measure of the variability of cycle lengthwithin an electrogram and spatially among all or selected electrogramscan be quantified to estimate cycle length variability. For instance,reciprocal of the dominant frequency can be used to calculate mean cyclelength.

A spatial time-domain approach to determining cycle length is byselecting sites that are representative of cycle length and using thatvalue to derive auto-relation indices with all other spatialelectrograms. This could be displayed as a spatial map showing regionsthat belong to the particular cycle length and sites that are outliers.This process can be repeated for other electrogram-cycle lengthcombinations to quickly estimate regions of variable and outlier cyclelength that are of significant in identifying treatment targets.

This cycle length and regions of its variability is presented as aspatial 3D map, the cycle length map (CLM). The variability anddispersion of CL can also be presented as a 3D map.

By way of example FIG. 15 depicts an example of a GUI 400 in which a CFEmap is superimposed on the surface of a graphical representation of aheart model 402. In the example of FIG. 15, the GUI 400 includes a mapof minimum activation front cycle length, such as can computed for aplurality of points on the surface of the heart based on multipleactivations for each point for a selected interval. In the example ofFIG. 15, two virtual electrodes 404 and 406 have been positioned atuser-selected locations. Electrograms 410 and 412 are generated in anadjacent window 408 based on electroanatomic data and the location datafor each of the virtual electrodes 404 and 406.

A time interval has also been set. Similar to described herein, a buttonor other user interface element 414 can be utilized by a user toactivate an interval selection function. In response to activation ofthe interval selector, respective calipers 416 and 418 are provided foradjusting the interval. The activation time and, in turn, cycle lengthsfor each of the plurality of points on the heart can be computedaccording to the interval. A corresponding static representation of theminimum cycle length for each point on the heart can be representedgraphically, such as shown in FIG. 15. A corresponding scale can beprovided so that a user can discern what the minimum cycle length isover the surface of the heart 402.

FIG. 15 also depicts amplitude calipers 420 and 422 that can beactivated and set to define amplitude range for the map that is beinggenerated. Thus, electrograms that do not fall within the selected rangecan be excluded from consideration when computing activation times anddetermining the minimum (or other statistical quantity) from theactivation times.

FIG. 16 depicts another example of another GUI 430 that can beimplemented. In this example, a propagation map superimposed on a 3Drepresentation of a patient's heart is depicted at 432 for a giveninstance in time. It is to be understood and appreciated that thepropagation map (corresponding to propagation of activation times on theepicardial surface of the heart) varies as a function of time accordingto the electrical activity of the heart. The activation times for aplurality of points on the surface can be computed, such as according tomethods shown and described herein. In the example of FIG. 16, twovirtual electrodes 434 and 436 are placed at user-selected locations onthe graphical representation of the patient's heart 432, responsive towhich an adjacent electrogram window 438 displays correspondingelectrograms 442 and 444.

An interval selector can be activated by selecting an interval selectoruser interface element 440. In response to activation of the intervalselector, respective calipers 446 and 448 are provided for selectivelyadjusting the interval. The activation time for each of the plurality ofpoints on the heart can be computed according to the selected interval.

In the example, of FIG. 16, the propagation of the activation front canbe dynamically visualized as an animated map. For instance, this dynamicvisualization can be visualized and selectively displayed to a userbased on the use of a “CINE” caliper function of the available tools.For example, the GUI 430 can include a GUI element that can be selectedto present the user with the cursor or caliper 450 that is superimposedon the electrogram window 438 for movement between the interval calipers446 and 448. IN the example, FIG. 16, the cursor 450 is depicted as avertical line that is moveable horizontally in the electrogram window438. The horizontal position of the CINE caliper corresponds to acurrent time at which activation information is displayed on the map at432. Those skilled in the art will appreciate that, additionally oralternatively, other means can be provided to identify the current timeassociated with map being displayed. For example, a clock or counter canbe provided to show the elapsed time for the dynamic map. Othergraphical elements can also be provided to demonstrate a current placein time for the electrogram for which the corresponding map is beingdisplayed. The CINE cursor 450 thus can traverse between the start andstop time calipers 446 and 448, which can be fixed while the dynamic mapis varied over time. The calipers, of course, can be adjusted as shownand described herein, which may be performed manually or automatically.

By way of further example, a user can drag the graphical representationof the cursor 450 to a desired position between the start and stop timeintervals 446 and 448, such as by using a mouse or other user inputdevice (e.g., a touch screen). As the CINE cursor is dragged across theelectrogram, the corresponding representation of the patient's heartwill be modified to display the information in the map frame accordingto the location of the CINE cursor 250 relative to the electrogram.Additionally or alternatively, a user can employ a corresponding userinterface element to cause a CINE cursor 450 to automatically moveacross the electrogram from the start time across the electrogram to thestop time. As the cursor automatically traverses from the start time tothe stop time, the corresponding potential map at 432 will change andreflect the current data according to the time associated with the CINEcursor.

Another workflow tool function can be utilized to set an appropriateuser interface element to cause the CINE cursor to ‘rock’ repeatedlyback and forth between the start and end calipers 446 and 448. Thisback-and-forth movement of the cursor affords a user an opportunity tovisualize changes in the potential map that is displayed at 240—both innormal forward time and in a reverse temporal direction.

A user can manually adjust each of the respective start and stoplocations 266 and 268 by dragging them to an appropriate location.Alternatively or additionally, the methods described herein can beutilized to automatically select a user defined interval such ascorresponding to a given heartbeat, or a type of heartbeat or a set ofone or more beats or a period that is centered about a given type ofelectro activity.

Organization Index (OI)

During an Afib ablation procedure, chaotic rhythms often organizethemselves into more organized rhythms including flutter andtachycardia. An organization index can be derived by measuring theperiodicity of electrograms over the entire heart for each real timeelectrogram segment. An OI of 1 indicates highly periodic activity likemacro-reentrant flutter. Regions of uniform cycle length in an intervalwill be displayed as highly organized regions. An OI indicator in theGUI will be very useful in quantifying the number of times and whatpoints the rhythm became organized during the procedure.

Paced Mapping

As a special case, the analysis techniques described in 1-4 can be usedin the context of paced mapping. Paced mapping involves stimulatingdifferent sites on the heart to elicit a ‘paced beats’. Pacing can beperformed at sites identified by the aforementioned techniques. Theresulting paced beats can be analyzed and presented using the sametechniques described for complex electrical activity. The resultant mapscan be presented in cardioframes alongside intrinsic electrical activitymaps and also queried and compared. Maps for intrinsic activity andpaced activity can be compared spatially either visually or usingmathematical measures like correlation coefficients also displayed as a3D spatial map.

Repolarization Mapping

The recovery of the each region of the heart following activation can beanalyzed to understand the recovery of the heart. Unlike activation,recovery of the heart is a property of each cardiac cell. Disease altersthe properties of the cells, thereby altering global repolarization.Also, a chamber that has been in chaotic activity for a period of timestarts to develop changes in its recovery. Mapping regional recoveryproperties has the potential to identify regions that may beinstrumental in maintaining and sustaining fibrillatory conduction.

Spatial recovery times can be estimated from the recovery component ofeach spatial electrogram (e.g., the T-wave) by calculating its firsttime derivative. The difference between this recovery time and theactivation time (previously described in the application) can becalculated for each spatial electrogram to give an estimated of thespatial Activation Recovery Interval (ARI) that represents localrecovery/repolarization properties. The ARI can be computed for each ofthe plurality points on the surface of the heart for whichelectroanatomic data (e.g., corresponding to electrograms) is available.This ARI can be displayed in the form of a spatial 3D map on the surfaceof the heart similar to other maps shown and described herein.

ARIs can be compared between anatomical regions of the heart to derivespatial estimates of the dispersion of ARIs (an indicator of underlyingdisease). For example, a user can select two or more virtual electrodesat points on the heart. The difference between ARIs for each pair ofvirtual electrode locations provides an indication of the dispersion forthe virtual electrode pair.

Example Operating Environment

FIG. 18 illustrates one example of a computer system 500 of the typethat can be utilized to implement one or more embodiments of the systemsand methods described herein for visualizing physiological data relatingto a patient's organ. The computer system 500 can be implemented on oneor more general purpose networked computer systems, embedded computersystems, routers, switches, server devices, client devices, variousintermediate devices/nodes and/or stand alone computer systems.Additionally, the computer system 500 or portions thereof can beimplemented on various mobile or portable clients such as, for example,a laptop or notebook computer, a personal digital assistant (PDA), andthe like.

The system bus 508 may be any of several types of bus structureincluding a memory bus or memory controller, a peripheral bus, and alocal bus using any of a variety of conventional bus architectures suchas PCI, VESA, Microchannel, ISA, and EISA, to name a few. The systemmemory 506 includes read only memory (ROM) 510 and random access memory(RAM) 512. A basic input/output system (BIOS), containing the basicroutines that help to transfer information between elements within thecomputer 502, such as during start-up, is stored in ROM 510.

The computer 502 also may include, for example, a hard disk drive 514, amagnetic disk drive 516, e.g., to read from or write to a removable disk518, and an optical disk drive 520, e.g., for reading from or writing toa CD-ROM disk 522 or other optical media. The hard disk drive 514,magnetic disk drive 516, and optical disk drive 520 are connected to thesystem bus 508 by a hard disk drive interface 524, a magnetic disk driveinterface 526, and an optical disk drive interface 528, respectively.The drives and their associated computer-readable media providenonvolatile storage of data, data structures, computer-executableinstructions, etc. for the computer 502. Although the description ofcomputer-readable media above refers to a hard disk, a removablemagnetic disk and a CD, it should be appreciated by those skilled in theart that other types of media which are readable by a computer, such asmagnetic cassettes, flash memory cards, digital video disks, Bernoullicartridges, and the like, may also be used in the exemplary operatingenvironment 500, and further that any such media may containcomputer-executable instructions for performing the methods of thepresent invention.

A number of program modules may be stored in the drives and RAM 512,including an operating system 530, one or more application programs 532,other program modules 534, and program data 536. The operating system530 in the computer 502 could be any suitable operating system orcombinations of operating systems. The application programs 516, otherprogram modules 517, and program data 518 can cooperate to provide avisualization of output results for a patient's organ, such as shown anddescribed herein.

A user may enter commands and information into the computer 502 throughone or more user input devices, such as a keyboard 538 and a pointingdevice (e.g., a mouse 540). Other input devices (not shown) may includea microphone, a joystick, a game pad, a satellite dish, a scanner, orthe like. These and other input devices are often connected to theprocessing unit 504 through a serial port interface 542 that is coupledto the system bus 508, but may be connected by other interfaces, such asa parallel port, a game port or a universal serial bus (USB). A monitor544 or other type of display device is also connected to the system bus508 via an interface, such as a video adapter 546. In addition to themonitor 544, the computer 502 may include other peripheral outputdevices (not shown), such as speakers, printers, etc. Thus, the outputrepresentation for a virtual electrode is not limited to a graphicalrepresentation on a display.

The computer 502 may operate in a networked environment using logicalconnections to one or more remote computers 560. The remote computer 560may be a workstation, a server computer, a router, a peer device, orother common network node, and typically includes many or all of theelements described relative to the computer 502, although, for purposesof brevity, only a memory storage device 562 is illustrated in FIG. 15.The logical connections depicted in FIG. 15 may include a local areanetwork (LAN) 564 and a wide area network (WAN) 566. Such networkingenvironments are commonplace in offices, enterprise-wide computernetworks, intranets, and the Internet.

When used in a LAN networking environment, the computer 502 is connectedto the local network 564 through a network interface or adapter 568.When used in a WAN networking environment, the computer 502 typicallyincludes a modem 570, or is connected to a communications server on anassociated LAN, or has other means for establishing communications overthe WAN 566, such as the Internet. The modem 570, which may be internalor external, is connected to the system bus 508 via the serial portinterface 542. In a networked environment, program modules depictedrelative to the computer 502, or portions thereof, may be stored in theremote memory storage device 562. It will be appreciated that thenetwork connections shown are exemplary and other means of establishinga communications link between the computers 502 and 560 may be used.

In accordance with the practices of persons skilled in the art ofcomputer programming, the present invention has been described withreference to acts and symbolic representations of operations that areperformed by a computer, such as the computer 502 or remote computer560, unless otherwise indicated. Such acts and operations are sometimesreferred to as being computer-executed. It will be appreciated that theacts and symbolically represented operations include the manipulation bythe processing unit 504 of electrical signals representing data bitswhich causes a resulting transformation or reduction of the electricalsignal representation, and the maintenance of data bits at memorylocations in the memory system (including the system memory 506, harddrive 514, floppy disks 518, CD-ROM 522, and shared storage system 510)to thereby reconfigure or otherwise alter the computer system'soperation, as well as other processing of signals. The memory locationswhere such data bits are maintained are physical locations that haveparticular electrical, magnetic, or optical properties corresponding tothe data bits.

In view of the features shown and described herein, those skilled in theart will understand and appreciated various modifications andimplementations of spatial visualizations that can be utilized. As anexample, a user can select a variety of other functions viacorresponding user interface elements that can be provided on acardioframe. For instance, a user can isolate one or more beats from oneor more electrograms. The isolation of heartbeats can be a manualprocedure, such as by using calipers similar to those shown anddescribed herein. Alternatively or additional, the identification andisolation of a given heartbeat can be automated by performingcorresponding methods.

Additionally, in one embodiment, data is acquired concurrently asepicardial data for the entire heart. As a result of acquiring data inthis manner, the representation of the data being presented (e.g., in acardioframe) can correspond to electrical activity for a single chamber,for two chambers, for three chambers or for all four chambers of thepatient's heart. Since the data is acquired concurrently for allchambers, a user can employ methods shown and described herein toperform comparisons on any number of chambers. Such comparisons caninclude temporal comparisons for multiple heart chambers for an isolatedbeat of interest. The visual representation further can providecomparisons for selected chambers of the patient's heart which canfurther be presented to the user.

As a further example, the methods can be utilized to calculate minimumor maximum activation times for each of one or more intervals ofinterest. Methods can also be utilized to compute standard deviations,statistics across the one or more intervals that have been selected.

The approach described herein further can facilitate pattern matching.For example, pattern matching methods can be employed to determine (to adegree of statistical likelihood) the repeatability of patterns. Thuspatterns can be detected and analyzed further based upon the methodsshown and described herein. The pattern matching can be utilized in thefrequency domain spectrum or in the time domain spectrum.

One further functionality that can be associated with the cardioframeand, in particular, with the isochrone maps, is to identify a ‘line ofblock.’ The line of block can be computed, for example, based on thegradient calculated for a given isochrones map. Methods can thus beutilized to analyze the gradient of the isochrones map as well as tocompute maximum conduction velocities. Based on further analysis of thegradient over a plurality of heart beats, it can be determined whetherthe line of block corresponds to a functional condition associated withthe heart, which changes beat to beat and then usually not repeatable.It can also be determined from such analysis whether such line of blockmight correspond to an anatomical condition, such as may be due to astructural defect since the line of block may be repeatable for a givenregion of the patient's heart.

What have been described above are examples and embodiments of theinvention. It is, of course, not possible to describe every conceivablecombination of components or methodologies for purposes of describingthe invention, but one of ordinary skill in the art will recognize thatmany further combinations and permutations of the invention arepossible. Accordingly, the invention is intended to embrace all suchalterations, modifications and variations that fall within the scope ofthe appended claims. In the claims, unless otherwise indicated, thearticle “a” is to refer to “one or more than one.”

What is claimed is:
 1. A computer implemented method, comprising:storing electroanatomic data in memory, the electroanatomic datarepresenting electrical activity for an anatomic region within apatient's body; selecting a time interval for the electrical activity inresponse to a user selection, wherein the user selection specifies theinterval based on interacting with a representation of a waveform ofelectrical activity for a given location of a plurality of locations forthe anatomic region; and responsive to the user selection of the timeinterval, automatically generating a visual representation ofphysiological information for the user selected time interval byapplying at least one analysis method to the electroanatomic data foreach of the plurality of locations of the anatomic region within theuser selected interval, the visual representation of physiologicalinformation for the user selected time interval being spatiallyrepresented on a graphical representation of at least a portion of theanatomic region.
 2. The method of claim 1, further comprising: receivinga user input that modifies the user selected interval; re-applying theat least one method to the electroanatomic data for each of theplurality of locations of the anatomic region to compute the at leastone analysis method for each of a plurality of points across theanatomic region according to the modified user selected interval; anddynamically modifying the visual representation based on there-application of the at least one analysis method to theelectroanatomic data for each of the plurality of locations of theanatomic region within the modified user selected interval.
 3. Themethod of claim 2, wherein a plurality of activation times are computedfor each of the plurality of points, the method further comprising:determining a cycle length between sequential activation times for eachof the plurality of points; and generating the visual representation asspatial map indicative of at least one of cycle length and a numberactivation times for each of the plurality of points.
 4. The method ofclaim 2, further comprising setting a user-defined cycle length inresponse to a user input; and generating the visual representation as amap superimposed on the predetermined region based on anauto-correlation of the user-defined cycle length and the activationtimes computed for each the plurality of points.
 5. The method of claim1, wherein the at least one analysis method is programmed to analyzefrequency for a plurality of points on the predetermined anatomic regionwithin the patient's body by computing a frequency spectrum for each theplurality of points according to the user selected interval.
 6. Themethod of claim 1, further comprising: determining a dominant frequencyin the frequency spectrum for each the plurality of points; andgenerating the visualization to include a dominant frequency map on thegraphical representation of the anatomic region.
 7. The method of claim1, further comprising selecting the at least one analysis method from aplurality of preprogrammed analysis methods in response to a userselection input, the plurality of preprogrammed methods comprisinginstructions programmed to determine at least two of activation time,frequency spectrum information, dominant frequency, voltage potential,and electrogram fractionation.
 8. A computer implemented method,comprising: storing electroanatomic data in memory, the electroanatomicdata representing electrical activity for an anatomic region within apatient's body:, receiving a user input that defines location datacorresponding to a user-selected location for at least one virtualelectrode on a graphical representation of the anatomic region withinthe patient's body; generating a corresponding waveform of electricalactivity for each virtual electrode based on the location data and theelectroanatomic data for the received user input; selecting a timeinterval for the electrical activity in response to a user input,wherein the user input specifies the interval based on interacting withthe corresponding waveform of electrical activity for the at least onevirtual electrode; and responsive to the user selection of the timeinterval, generating a visual representation of physiologicalinformation for the user selected time interval by applying at least oneanalysis method to the electroanatomic data for the anatomic region, thevisual representation being spatially represented on a graphicalrepresentation of at least a portion of the anatomic region.
 9. Themethod of claim 8, wherein the visual representation comprises agraphical representation of an electroanatomic map, the method furthercomprising superimposing a graphical representation of the at least onevirtual electrode on the interactive graphical representation of theanatomic region within the patient's body in response to the receivingof the user input.
 10. The method of claim 1, wherein the user selectedinterval defines a start time and an end time that span the timeinterval, the method further comprising dynamically modifying the visualrepresentation in an animated manner to represent temporal changes inthe physiological information within the time interval spatiallydepicted by the graphical representation of the anatomic region withinthe patient's body.
 11. The method of claim 10, further comprisinggenerating an indication of a current time in the interval correspondingto the dynamically modified the visual representation.
 12. The method ofclaim 11, wherein the interval is selected from an interactive graphicalrepresentation of temporal electrophysiology information for at leastone point on the anatomic region within the patient's body, the methodfurther comprising repeatedly traversing the interval between the starttime and the stop time and dynamically modifying the visualrepresentation based on data associated with a current time during eachtraversal of the interval.
 13. The method of claim 1, further comprisingapplying an inverse method to compute the electroanatomical data for theanatomic region within the patient's body based on patient electricaldata acquired for the patient via non-invasive body surface electrodesand geometry data.
 14. The method of claim 13, wherein the geometry datacomprises at least one of a generic representation of the anatomicregion within the patient's body or a patient-specific representation ofthe anatomic region within the patient's body derived from imaging datafor the patient.
 15. The method of claim 1, further comprising:preprocessing electrical data acquired from a patient, the preprocessingincluding selecting at least one sample interval for the acquiredelectrical data in response to a user input, the electroanatomic databeing generated from the acquired electrical data for the selected atleast one sample interval, and wherein the selected interval is ananalysis interval, the electroanatomic data being generated according tothe electrical data acquired for the at least one sample interval, thevisual representation of physiological information for the user selectedinterval being generated by applying the at least one analysis method tothe electroanatomic data that is generated according to the at least onesample interval, such that the visual representation is variable andresponsive to modifications in the analysis interval based on the userselection thereof, while the at least one sample interval remains fixed.16. The method of claim 15, wherein the at least one sample intervalcomprises multiple non-temporally contiguous sample intervals, themethod further comprising aggregating the acquired electrical data foreach of the multiple non-temporally contiguous sample intervals toprovide a spliced set of electrical data, the electroanatomic data beinggenerated based on the spliced set of electrical data.
 17. Anon-transitory computer readable medium having instructions forperforming a method comprising: preprocessing electrical data acquiredfrom a patient, the preprocessing including selecting at least onesample interval for the acquired electrical data in response to a userinput; generating electroanatomic data for an anatomic region ofinterest based on the acquired electrical data for the selected at leastone sample interval; storing the electroanatomic data for an anatomicregion of interest; selecting an analysis time interval for theelectrical activity in response to a user selection, wherein the userselection specifies the analysis time interval based on interacting witha representation of a waveform of electrical activity for a givenlocation of the anatomic region; selecting at least one analysis methodin response to a user input; and generating a visual representation ofphysiological information for the analysis time interval by applying theselected at least one analysis method to the electroanatomic data thatis generated according to the selected at least one sample interval,such that the visual representation is variable and responsive tomodifications in the analysis interval based on the user selectionthereof, while the at least one sample interval remains fixed, thevisual representation being spatially represented on a graphicalrepresentation of at least a portion of the anatomic region of interest.18. The medium of claim 17, wherein the method further comprisesapplying an inverse method to compute the electroanatomical data for theanatomic region of interest based on the acquired electrical data for atleast the selected at least one sample interval, the acquired electricaldata being acquired for the patient via non-invasive body surfaceelectrodes and geometry data, the geometry data corresponding to atleast one of geometry of anatomy of the patient or mathematical model.19. The medium of claim 17, wherein the at least one analysis method isselected from a set of preprogrammed analysis methods or is programmedin response to a user input.
 20. The method of claim 1, wherein the atleast one method is programmed to dynamically identify one or moreanatomical locations that meet user-defined criteria and toautomatically generate the visual representation to include a virtualidentifier at each location of the anatomic region that satisfies theuser-defined criteria.